Recent advances in Large Language Models have transformed Emotion Recognition in Conversation, yet existing instruction-tuned approaches still produce high-confidence misjudgments on utterances whose surface text is weakly informative but whose emotion depends on the surrounding situation. We propose SRERC, a selective reflective correction framework for LLM-based ERC. SRERC uses a verdict teacher to assess contextual support for the first-pass prediction and triggers reflective decoding only when reflection has an actionable role. For selected cases, an SRC generator constructs a Scene-aware Reflection Context, which provides target-specific situational grounding and verification probes for an SRC-conditioned second pass. This gated design enables targeted correction while preserving non-gated predictions and limiting unnecessary over-correction. Experiments on IEMOCAP, MELD, and EmoryNLP show that SRERC achieves the highest average weighted-F1 across the three benchmarks, with diagnostic gains on context-sensitive cases where surface cues are insufficient or misleading.