Existing emotion recognition methods mainly focus on enhancing performance by employing complex deep models, typically resulting in significantly higher model complexity. Although effective, it is also crucial to ensure the reliability of the final decision, especially for noisy, corrupted, and out-of-distribution data. To this end, we propose a novel emotion recognition method called Trusted Emotion Recognition (TER), which utilizes uncertainty estimation to calculate the confidence value of predictions. TER combines the results from multiple modalities based on their confidence values to output the trusted predictions. We also provide a new evaluation criterion to assess the reliability of predictions. Specifically, we incorporate trusted precision and trusted recall to determine the trusted threshold and formulate the trusted accuracy and trusted F1-score to evaluate the model’s trusted performance. The proposed framework combines the confidence module that accordingly endows the model with reliability and robustness against possible noise or corruption. The extensive experimental results validate the effectiveness of our proposed model. The TER achieves state-of-the-art performance on the Music-video dataset, achieving 82.40% accuracy In terms of trusted performance, TER outperforms other methods on the IEMOCAP and Music-video datasets, achieving trusted F1-scores of 0.7511 and 0.9035, respectively.