  Noisy labels can significantly affect the performance of deep neural networks
(DNNs). In medical image segmentation tasks, annotations are error-prone due to
the high demand in annotation time and in the annotators' expertise. Existing
methods mostly assume noisy labels in different pixels are \textit{i.i.d}.
However, segmentation label noise usually has strong spatial correlation and
has prominent bias in distribution. In this paper, we propose a novel Markov
model for segmentation noisy annotations that encodes both spatial correlation
and bias. Further, to mitigate such label noise, we propose a label correction
method to recover true label progressively. We provide theoretical guarantees
of the correctness of the proposed method. Experiments show that our approach
outperforms current state-of-the-art methods on both synthetic and real-world
noisy annotations.
