  Neural networks for automated image segmentation are typically trained to
achieve maximum accuracy, while less attention has been given to the
calibration of their confidence scores. However, well-calibrated confidence
scores provide valuable information towards the user. We investigate several
post hoc calibration methods that are straightforward to implement, some of
which are novel. They are compared to Monte Carlo (MC) dropout. They are
applied to neural networks trained with cross-entropy (CE) and soft Dice (SD)
losses on BraTS 2018 and ISLES 2018. Surprisingly, models trained on SD loss
are not necessarily less calibrated than those trained on CE loss. In all
cases, at least one post hoc method improves the calibration. There is limited
consistency across the results, so we can't conclude on one method being
superior. In all cases, post hoc calibration is competitive with MC dropout.
Although average calibration improves compared to the base model, subject-level
variance of the calibration remains similar.
