I have a regression problem with very few datapoints, therefore I want to use leave-one-out cross validation (effectively N-fold cross validation with N being the number of datapoints) to determine model performance on unseen data. I would like to compute the coefficient of determination $R^2$ because of its straightforward interpretation as the variance explained which clearly indicates how well the model will perform on new data. However, since I have only one predicted target value for each of the N folds, $R^2$ is zero unless the predicted value coincides exactly with the true target value. This seems to render the application of $R^2$ useless in this case. Are there other metrics I may use with a similar straightforward interpretation? What other ways exist to approach this problem?
P.S.: One alternative I came up with is computing $R^2$ using the set of all predicted target values (from all N folds) and the true target values. However, this leaves me without an estimation of its variance.