Many measurement systems operate where ground truth is sparse, biased, or prohibitively expensive — and scarcest exactly where uncertainty is highest. Self-Validating Uncertainty argues that validation should shift from correctness to calibration, and that measurement systems should expose the evidence, uncertainty, and assumptions needed to assess and challenge their own reliability claims.