  Turning pass-through network architectures into iterative ones, which use
their own output as input, is a well-known approach for boosting performance.
In this paper, we argue that such architectures offer an additional benefit:
The convergence rate of their successive outputs is highly correlated with the
accuracy of the value to which they converge. Thus, we can use the convergence
rate as a useful proxy for uncertainty. This results in an approach to
uncertainty estimation that provides state-of-the-art estimates at a much lower
computational cost than techniques like Ensembles, and without requiring any
modifications to the original iterative model. We demonstrate its practical
value by embedding it in two application domains: road detection in aerial
images and the estimation of aerodynamic properties of 2D and 3D shapes.
