  While computer vision has proven valuable for medical image segmentation, its
application faces challenges such as limited dataset sizes and the complexity
of effectively leveraging unlabeled images. To address these challenges, we
present a novel semi-supervised, consistency-based approach termed the
data-efficient medical segmenter (DEMS). The DEMS features an encoder-decoder
architecture and incorporates the developed online automatic augmenter (OAA)
and residual robustness enhancement (RRE) blocks. The OAA augments input data
with various image transformations, thereby diversifying the dataset to improve
the generalization ability. The RRE enriches feature diversity and introduces
perturbations to create varied inputs for different decoders, thereby providing
enhanced variability. Moreover, we introduce a sensitive loss to further
enhance consistency across different decoders and stabilize the training
process. Extensive experimental results on both our own and three public
datasets affirm the effectiveness of DEMS. Under extreme data shortage
scenarios, our DEMS achieves 16.85\% and 10.37\% improvement in dice score
compared with the U-Net and top-performed state-of-the-art method,
respectively. Given its superior data efficiency, DEMS could present
significant advancements in medical segmentation under small data regimes. The
project homepage can be accessed at https://github.com/NUS-Tim/DEMS.
