  2D echocardiography is the most common imaging modality for cardiovascular
diseases. The portability and relatively low-cost nature of Ultrasound (US)
enable the US devices needed for performing echocardiography to be made widely
available. However, acquiring and interpreting cardiac US images is operator
dependent, limiting its use to only places where experts are present. Recently,
Deep Learning (DL) has been used in 2D echocardiography for automated view
classification, and structure and function assessment. Although these recent
works show promise in developing computer-guided acquisition and automated
interpretation of echocardiograms, most of these methods do not model and
estimate uncertainty which can be important when testing on data coming from a
distribution further away from that of the training data. Uncertainty estimates
can be beneficial both during the image acquisition phase (by providing
real-time feedback to the operator on acquired image's quality), and during
automated measurement and interpretation. The performance of uncertainty models
and quantification metric may depend on the prediction task and the models
being compared. Hence, to gain insight of uncertainty modelling for left
ventricular segmentation from US images, we compare three ensembling based
uncertainty models quantified using four different metrics (one newly proposed)
on state-of-the-art baseline networks using two publicly available
echocardiogram datasets. We further demonstrate how uncertainty estimation can
be used to automatically reject poor quality images and improve
state-of-the-art segmentation results.
