  Uncertainty of labels in clinical data resulting from intra-observer
variability can have direct impact on the reliability of assessments made by
deep neural networks. In this paper, we propose a method for modelling such
uncertainty in the context of 2D echocardiography (echo), which is a routine
procedure for detecting cardiovascular disease at point-of-care. Echo imaging
quality and acquisition time is highly dependent on the operator's experience
level. Recent developments have shown the possibility of automating echo image
quality quantification by mapping an expert's assessment of quality to the echo
image via deep learning techniques. Nevertheless, the observer variability in
the expert's assessment can impact the quality quantification accuracy. Here,
we aim to model the intra-observer variability in echo quality assessment as an
aleatoric uncertainty modelling regression problem with the introduction of a
novel method that handles the regression problem with categorical labels. A key
feature of our design is that only a single forward pass is sufficient to
estimate the level of uncertainty for the network output. Compared to the $0.11
\pm 0.09$ absolute error (in a scale from 0 to 1) archived by the conventional
regression method, the proposed method brings the error down to $0.09 \pm
0.08$, where the improvement is statistically significant and equivalents to
$5.7\%$ test accuracy improvement. The simplicity of the proposed approach
means that it could be generalized to other applications of deep learning in
medical imaging, where there is often uncertainty in clinical labels.
