  Counting is a fundamental task in biomedical imaging and count is an
important biomarker in a number of conditions. Estimating the uncertainty in
the measurement is thus vital to making definite, informed conclusions. In this
paper, we first compare a range of existing methods to perform counting in
medical imaging and suggest ways of deriving predictive intervals from these.
We then propose and test a method for calculating intervals as an output of a
multi-task network. These predictive intervals are optimised to be as narrow as
possible, while also enclosing a desired percentage of the data. We demonstrate
the effectiveness of this technique on histopathological cell counting and
white matter hyperintensity counting. Finally, we offer insight into other
areas where this technique may apply.
