Predicting Uncertainty in Random Forest Regression Scenario: I'm trying to build a random forest regressor to accelerate probing a large phase space.  I'm using python/scikit-learn to perform the regression, and I'm able to obtain a model that has a reasonably low cross-validation error on known data split into training/test sets.
Now I'd like to begin asking my model how confident it is (I'm a bit confused about the difference between confidence intervals and prediction intervals).  Currently I know how to return some estimator $y$ given data $x$, and now I'd like to be able to get a measure of uncertainty.  If the model is sufficiently uncertain, I'd like it to let me know so that I can add that case to the training set.
My impression is that random forests are an ensemble methods.  We grow $N$ decision trees, and then our predictor is given by:
$$
y = \sum_i^N w_iT_i\left(x\right)
$$
Where $w_i$ is some weight and $T_i(x)$ is the value predicted by the $i$-th tree.
Perhaps the simplest (and by no means most promising) approach would be to try to take something like a variance:
$$
\sigma^2 = \sum_i^N \left(T_i(x) - y(x)\right)^2
$$
This guide seems to suggest we can do quantile analysis using each tree as an observation.  Unfortunately I'm not sure I follow that logic, because it seems to me that even for data points that were in the training set that there should be some variance among the trees.
This post seems to mention the flaws in the above methods, however I unfortunately can't really follow what they suggest we do instead (as my skills in R aren't what they could be).  Can someone weigh in on whether this is the train I should be following, and perhaps help me understand what's going on?  I can't understand where to work in assumptions about the sample.  Seeing similar code in Python would likely be really helpful to me.
This paper also seems like it might be useful, but the terms/notations are entirely outside of my specialty, and its essentially unreadable to me right now.
While I've worked in statistics on an application level, it's been a while since my last formal class.  So part of the barrier to entry for me is terminology/symbol use.
Edited to provide more detail.
 A: As far as I know, the uncertainty of the RF predictions can be estimated using several approaches, one of them is the quantile regression forests method(Meinshausen, 2006), which estimates the prediction intervals. Other methods include U-statistics approach of Mentch & Hooker (2016) and monte carlo simulations approach of Coulston (2016).
A: The problem of constructing prediction intervals for random forest predictions has been addressed in the following paper:
Zhang, Haozhe, Joshua Zimmerman, Dan Nettleton, and Daniel J. Nordman. "Random Forest Prediction Intervals." The American Statistician,2019. 
The R package "rfinterval" is its implementation available at CRAN.
Installation
To install the R package rfinterval:
#install.packages("devtools")
#devtools::install_github(repo="haozhestat/rfinterval")
install.packages("rfinterval")
library(rfinterval)
?rfinterval

Usage
Quickstart:
train_data <- sim_data(n = 1000, p = 10)
test_data <- sim_data(n = 1000, p = 10)

output <- rfinterval(y~., train_data = train_data, test_data = test_data,
                     method = c("oob", "split-conformal", "quantreg"),
                     symmetry = TRUE,alpha = 0.1)

### print the marginal coverage of OOB prediction interval
mean(output$oob_interval$lo < test_data$y & output$oob_interval$up > test_data$y)

### print the marginal coverage of Split-conformal prediction interval
mean(output$sc_interval$lo < test_data$y & output$sc_interval$up > test_data$y)

### print the marginal coverage of Quantile regression forest prediction interval
mean(output$quantreg_interval$lo < test_data$y & output$quantreg_interval$up > test_data$y)
``` 

