<p>Domain shifts, such as changes in language, noise types, or recording environments, can significantly degrade the performance of speech enhancement systems. Most current research focuses on domain adaptation methods. While existing research often assumes that domain shifts have already been identified, this study addresses the crucial challenge of detecting them automatically. We propose a novel domain-shift detection method that monitors prediction uncertainty in a speech quality assessment network. Our method includes both supervised and unsupervised approaches. The supervised method requires access to clean speech, whereas the unsupervised method does not. Experimental results across various domain-shift scenarios demonstrate that our method effectively identifies domain mismatches, enabling timely adaptation to improve performance in real-world speech enhancement systems. We use 100 s of noisy speech to detect actionable domain shifts, typically achieving an SI-SDR improvement of 3–6 dB after adaptation. To the best of our knowledge, this is the first method for domain-shift detection in speech enhancement, offering both supervised and unsupervised variants with broad applicability to real-world systems.</p>