  Modern deep neural networks achieved remarkable progress in medical image
segmentation tasks. However, it has recently been observed that they tend to
produce overconfident estimates, even in situations of high uncertainty,
leading to poorly calibrated and unreliable models. In this work we introduce
Maximum Entropy on Erroneous Predictions (MEEP), a training strategy for
segmentation networks which selectively penalizes overconfident predictions,
focusing only on misclassified pixels. Our method is agnostic to the neural
architecture, does not increase model complexity and can be coupled with
multiple segmentation loss functions. We benchmark the proposed strategy in two
challenging segmentation tasks: white matter hyperintensity lesions in magnetic
resonance images (MRI) of the brain, and atrial segmentation in cardiac MRI.
The experimental results demonstrate that coupling MEEP with standard
segmentation losses leads to improvements not only in terms of model
calibration, but also in segmentation quality.
