  The success of modern deep learning algorithms for image segmentation heavily
depends on the availability of large datasets with clean pixel-level
annotations (masks), where the objects of interest are accurately delineated.
Lack of time and expertise during data annotation leads to incorrect boundaries
and label noise. It is known that deep convolutional neural networks (DCNNs)
can memorize even completely random labels, resulting in poor accuracy. We
propose a framework to train binary segmentation DCNNs using sets of unreliable
pixel-level annotations. Erroneously labeled pixels are identified based on the
estimated aleatoric uncertainty of the segmentation and are relabeled to the
true value.
