Seismic data acquisition is essential for discovering new hydrocarbon targets, where a high-resolution regular-spaced acquisition is critical to obtain high-quality seismic images. However, the high acquisition costs and environmental impacts have motivated designing seismic surveys with fewer sources and receivers than regular-spaced sensing approaches. After the undersampled measurements are acquired, an algorithm reconstructs the missing information necessary for the subsequent processing and interpretation analysis. The removed data is currently selected using random, jittered, or uniform sensing schemes leading to suboptimal seismic image recovery. Therefore, a guided design of undersampled seismic surveys is important as it determines the quality of the reconstructed information. This work proposes an end-to-end optimization to design an undersampled seismic acquisition pattern that preserves the high quality of the reconstructed data. The sensing pattern is modeled as a deep binary layer to learn the location of receivers and sources for a particular seismic survey. Simultaneously, a deep neural network recovers the underlying removed data. Once the sensing pattern is designed, it can be used as a seismic acquisition geometry in an area that exhibits a similar geological setting to the training data set of the end-to-end model. Extensive experiments were conducted on synthetic and real seismic data from different geological settings. The proposed design was compared with the traditional random, jittered, and uniform sensing schemes. The results validate that a guided design improves the quality of the reconstructed data by up to 4 and 2 dB in Peak-Signal-to-Noise-Ratio for trace and shot gather reconstruction, respectively.