Deep learning (DL) has been widely used for various geological tasks but not much for seismic unconformity estimation because of the limited labeled training data. We propose the physical knowledge constrained synthetic data generation workflow for building the synthetic training dataset, which mainly contains the Data-assisted module, the Physics-assisted module, and the Math-assisted module. The Data-assisted module is used to extract geometry and reflection features of seismic events from field data. The Physics-assisted module is used to provide geological knowledge related to seismic unconformity and define the unconformity geometry criteria for simulating unconformities in synthetic seismic images. The Math-assisted module is used to modify extracted features, create synthetic seismic images, and generate unconformity labels with the aid of the Data- and Physics-assisted modules. Therefore, incorporating these three modules makes the synthetic seismic datasets closely resemble field data. The synthetic datasets are used to train the DL model that we proposed, named ECA-UNet++, which integrates the UNet++ with the Transformer block and efficient channel attention (ECA). To validate the performance of our proposed method, we apply it to synthetic data and two 3-D field data. The results demonstrate the effectiveness of our proposed method for accurate and efficient seismic unconformity estimation, even when trained exclusively on synthetic data. Furthermore, we integrate the estimated unconformity results into seismic facies analysis, indicating that our method benefits other seismic interpretation tasks.