Abstract
Calibrated uncertainty tells the lab which experiment will teach the model the most.
A prediction is only useful if you know how much to trust it.
We compare ways of estimating uncertainty and test them against real lab decisions.
The best methods cut the number of experiments needed to reach a target.
