Piano transcription systems are typically optimized to estimate pitch activity at each frame of audio. They are often followed by carefully designed heuristics and post-processing algorithms to estimate note events from the frame-level predictions. Recent methods have also framed piano transcription as a multi-task learning problem, where the activation of different stages of a note event are estimated independently. These practices are not well aligned with the desired outcome of the task, which is the specification of note intervals as holistic events, rather than the aggregation of disjoint observations. In this work, we propose a novel formulation of piano transcription, which is optimized to directly predict note events. Our method is based on Semi-Markov Conditional Random Fields (semi-CRF), which produce scores for intervals rather than individual frames. When formulating piano transcription in this way, we eliminate the need to rely on disjoint frame-level estimates for different stages of a note event. We conduct experiments on the MAESTRO dataset and demonstrate that the proposed model surpasses the current state-of-the-art for piano transcription. Our results suggest that the semi-CRF output layer, while still quadratic in complexity, is a simple, fast and well-performing solution for event-based prediction, and may lead to similar success in other areas which currently rely on frame-level estimates.