Multimodal emotion recognition (MER) in conversation is inherently ambiguous, as linguistic, acoustic, and visual cues may support multiple plausible interpretations. Existing approaches often treat ambiguity as prediction uncertainty rather than modeling its structured sources. We propose UAF, an ambiguity-aware framework for MER built around a single unified ambiguity signal. UAF estimates a normalized per-sample Model-based Ambiguity Degree (MAD) from the predicted emotion distribution by measuring its proximity to the maximally ambiguous uniform distribution, and uses it as a shared control signal rather than a post-hoc uncertainty score. This single scalar coordinates three parts of learning: it aligns representation radius with ambiguity, emphasizes ambiguous samples during optimization, and scales OT-based perturbation consistency. The fused evidence is organized into context, speaker, temporal, cross-modal, and residual subspaces that serve as the substrate on which this signal operates. Experiments on IEMOCAP, CMU-MOSEI, and MELD show that UAF yields consistently better calibration and high-ambiguity reliability alongside competitive accuracy and weighted-F1, showing that a single ambiguity signal can be an effective shared training signal for reliable MER