Automatic segmentation is an essential but challenging step for extracting quantitative imaging bio-markers for characterizing head and neck tumor in tumor detection, diagnosis, prognosis, treatment planning and assessment. The HEad and neCK TumOR Segmentation Challenge 2021 (HECKTOR 2021) provides a common platform for the following three tasks: 1) the automatic segmentation of the primary gross target volume (GTV) in the oropharynx region on FDG-PET and CT images; 2) the prediction of patient outcomes, namely Progression Free Survival (PFS) from the FDG-PET/CT images with automatic segmentation results and the available clinical data; and 3) the prediction of PFS with ground truth annotations. We participated in the first two tasks by further evaluating a fully automatic segmentation network based on encoder-decoder architecture. In order to better integrate information across different scales, we proposed a dynamic scale attention mechanism that incorporates low-level details with high-level semantics from feature maps at different scales. Radiomic features were extracted from the segmented tumors and used for PFS prediction. Our segmentation framework was trained using the 224 challenge training cases provided by HECKTOR 2021, and achieved an average Dice Similarity Coefficient (DSC) of 0.7693 with cross validation. By testing on the 101 testing cases, our model achieved an average DSC of 0.7608 and 95 % Hausdorff distance of 3.27 mm. The overall PFS prediction yielded a concordance index (c-index) of 0.53 on the testing dataset (id: deepX).