  Semi-supervised segmentation tackles the scarcity of annotations by
leveraging unlabeled data with a small amount of labeled data. A prominent way
to utilize the unlabeled data is by consistency training which commonly uses a
teacher-student network, where a teacher guides a student segmentation. The
predictions of unlabeled data are not reliable, therefore, uncertainty-aware
methods have been proposed to gradually learn from meaningful and reliable
predictions. Uncertainty estimation, however, relies on multiple inferences
from model predictions that need to be computed for each training step, which
is computationally expensive. This work proposes a novel method to estimate the
pixel-level uncertainty by leveraging the labeling representation of
segmentation masks. On the one hand, a labeling representation is learnt to
represent the available segmentation masks. The learnt labeling representation
is used to map the prediction of the segmentation into a set of plausible
masks. Such a reconstructed segmentation mask aids in estimating the
pixel-level uncertainty guiding the segmentation network. The proposed method
estimates the uncertainty with a single inference from the labeling
representation, thereby reducing the total computation. We evaluate our method
on the 3D segmentation of left atrium in MRI, and we show that our uncertainty
estimates from our labeling representation improve the segmentation accuracy
over state-of-the-art methods.
