  Semi-supervised learning relaxes the need of large pixel-wise labeled
datasets for image segmentation by leveraging unlabeled data. A prominent way
to exploit unlabeled data is to regularize model predictions. Since the
predictions of unlabeled data can be unreliable, uncertainty-aware schemes are
typically employed to gradually learn from meaningful and reliable predictions.
Uncertainty estimation methods, however, rely on multiple inferences from the
model predictions that must be computed for each training step, which is
computationally expensive. Moreover, these uncertainty maps capture pixel-wise
disparities and do not consider global information. This work proposes a novel
method to estimate segmentation uncertainty by leveraging global information
from the segmentation masks. More precisely, an anatomically-aware
representation is first learnt to model the available segmentation masks. The
learnt representation thereupon maps the prediction of a new segmentation into
an anatomically-plausible segmentation. The deviation from the plausible
segmentation aids in estimating the underlying pixel-level uncertainty in order
to further guide the segmentation network. The proposed method consequently
estimates the uncertainty using a single inference from our representation,
thereby reducing the total computation. We evaluate our method on two publicly
available segmentation datasets of left atria in cardiac MRIs and of multiple
organs in abdominal CTs. Our anatomically-aware method improves the
segmentation accuracy over the state-of-the-art semi-supervised methods in
terms of two commonly used evaluation metrics.
