  Probabilistic solvers for ordinary differential equations assign a posterior
measure to the solution of an initial value problem. The joint covariance of
this distribution provides an estimate of the (global) approximation error. The
contraction rate of this error estimate as a function of the solver's step size
identifies it as a well-calibrated worst-case error, but its explicit numerical
value for a certain step size is not automatically a good estimate of the
explicit error. Addressing this issue, we introduce, discuss, and assess
several probabilistically motivated ways to calibrate the uncertainty estimate.
Numerical experiments demonstrate that these calibration methods interact
efficiently with adaptive step-size selection, resulting in descriptive, and
efficiently computable posteriors. We demonstrate the efficiency of the
methodology by benchmarking against the classic, widely used Dormand-Prince 4/5
Runge-Kutta method.
