  In ultrasound shear wave elasticity (SWE) imaging, a number of algorithms
exist for estimating the shear wave speed (SWS) from spatiotemporal
displacement data. However, no method provides a well-calibrated and practical
uncertainty metric, hindering SWE's clinical adoption and utility in downstream
decision-making. Here, we designed a deep learning SWS estimator that
simultaneously outputs a quantitative and well-calibrated uncertainty value for
each estimate. Our deep neural network (DNN) takes as input a single 2D
spatiotemporal plane of tracked displacement data and outputs the two
parameters $m$ and $\sigma$ of a log-normal probability distribution. For
training and testing, we used in vivo 2D-SWE data of the cervix collected from
30 pregnant subjects, totaling 551 acquisitions and >2 million space-time
plots. Points were grouped by uncertainty into bins to assess uncertainty
calibration: the predicted uncertainty closely matched the root-mean-square
estimation error, with an average absolute percent deviation of 3.84%. We
created a leave-one-out ensemble model that estimated uncertainty with better
calibration (1.45%) than any individual ensemble member on a held-out patient's
data. Lastly, we applied the DNN to an external dataset to evaluate its
generalizability. We have made the trained model, SweiNet, openly available to
provide the research community with a fast SWS estimator that also outputs a
well-calibrated estimate of the predictive uncertainty.
