Machine learning (ML)-based transient stability assessment (TSA) provides extraordinary accuracy performance while limited by potential misjudgment risks. To address this issue, this letter originally develops a generic scene-dependent credibility evaluation (SCE) framework. The variance upper bound of ML model prediction error is inferred using an improved localized generalization error estimation (ILGEE) method, and the probability density of system stability is furtherly described as a Gaussian distribution incorporating Neumann boundary condition. Then the scene-dependent credibility index (SCI) is ultimately derived and defined as the information entropy implying the uncertainty of TSA results. Case studies verify the validity of the SCE framework and demonstrate the promising 100% accurate TSA performance with critical proposed SCI as 0.93.