RandomForest MeanDecreaseAccuracy interpretation

I know there are already some questions regarding the interpretation of the MeanDecreaseAccuracy metric of the randomForest-package, but it's still unclear to me. My assumption was that each variable is replaced by a randomly permutated version and the OOB error rate is recalculated. The increase of the error rate tells me the importance of the variable.

So let's take the variable "Start" in the example below. Can I say, that the expected increase in the current error rate (20.99%) is (15.8%-->20.99+15.8) if I would remove "Start" from my model?

I guess not, but how would the correct formulation sound like?

Thanks!

library(rpart)
library(randomForest)
set.seed(123)

fit <- randomForest(Kyphosis ~ Age + Number + Start , data=kyphosis, importance=TRUE, ntree=500)
print(fit)
importance(fit)

OOB estimate of  error rate: 20.99%
Confusion matrix:
absent present class.error
absent      59       5   0.0781250
present     12       5   0.7058824

> importance(fit)
absent   present MeanDecreaseAccuracy MeanDecreaseGini
Age    1.47962289  5.177725             4.347754         8.617383
Number 0.06287608  3.407481             2.909213         5.474125
Start  9.45380414 14.290110            15.809382         9.977147