We show that the adaptive diagnosis approach can be applied to probabilistically diagnosable systems. Three adaptive diagnosis algorithms are developed under both the symmetric and asymmetric test invalidation assumptions. A test selection strategy based on a probabilistic measure of test results is derived and used in each algorithm. We show that the adaptive algorithms are efficient in identifying all faulty units in a system.