There are several available methods to evaluate uncertainty predictions that take into account different aspects of uncertainty. Here we assess the quality of the uncertainty estimation by computing the calibration error curve, which is obtained by counting the fraction of test set datapoints that lie within a p confidence interval around the predicted value. Confidence intervals were modeled via Eq. ( ) on a single split. For a perfectly calibrated model, the observed, empirical coverage (fraction of test set with targets within each interval) should equal p, i.e. 95% of the test set should have a true target value within the 95% confidence interval spanned by the ensemble mean and variance of each prediction. The area under the calibration error curve, AUCE, measures the deviation of the observed calibration curve from perfect calibration. An AUCE of 0 corresponds to perfect calibration, larger values indicate an imperfect calibration.