  Neural network prediction probabilities and accuracy are often only
weakly-correlated. Inherent label ambiguity in training data for image
segmentation aggravates such miscalibration. We show that logit consistency
across stochastic transformations acts as a spatially varying regularizer that
prevents overconfident predictions at pixels with ambiguous labels. Our
boundary-weighted extension of this regularizer provides state-of-the-art
calibration for prostate and heart MRI segmentation.
