  Medical image segmentation annotations exhibit variations among experts due
to the ambiguous boundaries of segmented objects and backgrounds in medical
images. Although using multiple annotations for each image in the
fully-supervised has been extensively studied for training deep models,
obtaining a large amount of multi-annotated data is challenging due to the
substantial time and manpower costs required for segmentation annotations,
resulting in most images lacking any annotations. To address this, we propose
Multi-annotated Semi-supervised Ensemble Networks (MSE-Nets) for learning
segmentation from limited multi-annotated and abundant unannotated data.
Specifically, we introduce the Network Pairwise Consistency Enhancement (NPCE)
module and Multi-Network Pseudo Supervised (MNPS) module to enhance MSE-Nets
for the segmentation task by considering two major factors: (1) to optimize the
utilization of all accessible multi-annotated data, the NPCE separates
(dis)agreement annotations of multi-annotated data at the pixel level and
handles agreement and disagreement annotations in different ways, (2) to
mitigate the introduction of imprecise pseudo-labels, the MNPS extends the
training data by leveraging consistent pseudo-labels from unannotated data.
Finally, we improve confidence calibration by averaging the predictions of base
networks. Experiments on the ISIC dataset show that we reduced the demand for
multi-annotated data by 97.75\% and narrowed the gap with the best
fully-supervised baseline to just a Jaccard index of 4\%. Furthermore, compared
to other semi-supervised methods that rely only on a single annotation or a
combined fusion approach, the comprehensive experimental results on ISIC and
RIGA datasets demonstrate the superior performance of our proposed method in
medical image segmentation with ambiguous boundaries.
