How to estimate the variance of regressors in scikit-learn?

Question

Every classifier in scikit-learn has a method predict_proba(x) that predicts class probabilities for x. How to do the same thing for regressors?

The only regressor for which I know how to estimate the variance of the predictions is Gaussian process regression, for which I can do the following:

y_pred, sigma = gp.predict(x, return_std=True)

In one dimension, I can even plot, how confident the Gaussian process regressor is about its prediction of different data points

How to estimate the variance of predictions for other regressors? For example, for kernel ridge regressor, multi-layer perceptron, ensemble regressors?