  Imperfect labels limit the quality of predictions learned by deep neural
networks. This is particularly relevant in medical image segmentation, where
reference annotations are difficult to collect and vary significantly even
across expert annotators. Prior work on mitigating label noise focused on
simple models of mostly uniform noise. In this work, we explore biased and
unbiased errors artificially introduced to brain tumour annotations on MRI
data. We found that supervised and semi-supervised segmentation methods are
robust or fairly robust to unbiased errors but sensitive to biased errors. It
is therefore important to identify the sorts of errors expected in medical
image labels and especially mitigate the biased errors.
