  This study investigates barely-supervised medical image segmentation where
only few labeled data, i.e., single-digit cases are available. We observe the
key limitation of the existing state-of-the-art semi-supervised solution cross
pseudo supervision is the unsatisfactory precision of foreground classes,
leading to a degenerated result under barely-supervised learning. In this
paper, we propose a novel Compete-to-Win method (ComWin) to enhance the pseudo
label quality. In contrast to directly using one model's predictions as pseudo
labels, our key idea is that high-quality pseudo labels should be generated by
comparing multiple confidence maps produced by different networks to select the
most confident one (a compete-to-win strategy). To further refine pseudo labels
at near-boundary areas, an enhanced version of ComWin, namely, ComWin+, is
proposed by integrating a boundary-aware enhancement module. Experiments show
that our method can achieve the best performance on three public medical image
datasets for cardiac structure segmentation, pancreas segmentation and colon
tumor segmentation, respectively. The source code is now available at
https://github.com/Huiimin5/comwin.
