Uncertainty estimation for language models often treats reliability as a property of the answer distribution: high confidence, stable samples, or low semantic entropy are taken as signs that an answer can be trusted. Table reasoning exposes a fundamentally different failure mode. A model may consistently return the same answer while linking the wrong entity, selecting the wrong column, applying the wrong operation, or relying on incomplete or insufficient evidence. We introduce MIRAGE—Model Inference Reliability Assessment via Grounded Evidence, a structure-grounded reliability framework for selective table reasoning. MIRAGE estimates residual structural uncertainty from test-time traces, combining outcome consistency with grounding and computation signals, and produces calibrated answer-or-abstain decisions with lightweight reliability certificates. On a controlled four-family diagnostic benchmark, structural reliability signals substantially reduce selective risk compared with generic confidence, sampling, semantic-entropy, and hallucination-detection baselines.