  Consistency learning is a central strategy to tackle unlabeled data in
semi-supervised medical image segmentation (SSMIS), which enforces the model to
produce consistent predictions under the perturbation. However, most current
approaches solely focus on utilizing a specific single perturbation, which can
only cope with limited cases, while employing multiple perturbations
simultaneously is hard to guarantee the quality of consistency learning. In
this paper, we propose an Adaptive Bidirectional Displacement (ABD) approach to
solve the above challenge. Specifically, we first design a bidirectional patch
displacement based on reliable prediction confidence for unlabeled data to
generate new samples, which can effectively suppress uncontrollable regions and
still retain the influence of input perturbations. Meanwhile, to enforce the
model to learn the potentially uncontrollable content, a bidirectional
displacement operation with inverse confidence is proposed for the labeled
images, which generates samples with more unreliable information to facilitate
model learning. Extensive experiments show that ABD achieves new
state-of-the-art performances for SSMIS, significantly improving different
baselines. Source code is available at https://github.com/chy-upc/ABD.
