A prevalent family of fully convolutional networks are capable of learning discriminative representations and producing structural prediction in semantic segmentation tasks. However, such supervised learning methods require a large amount of labeled data and show inability of learning cross-domain invariant representations, giving rise to overfitting performance on the source dataset. Domain adaptation, a transfer learning technique that demonstrates strength on aligning feature distributions, can improve the performance of learning methods by providing inter-domain discrepancy alleviation. Recently introduced output-space based adaptation methods provide significant advances on cross-domain semantic segmentation tasks, however, a lack of consideration for intra-domain divergence of domain discrepancy remains prone to over-adaptation results on the target domain. To address the problem, we first leverage prototypical knowledge on the target domain to relax its hard domain label to a continuous domain space, where pixel-wise domain adaptation is developed upon a soft adversarial loss. The development of prototypical knowledge allows to elaborate specific adaptation strategies on under-aligned regions and well-aligned regions of the target domain. Furthermore, aiming to achieve better adaptation performance, we employ a unilateral discriminator to alleviate implicit uncertainty on prototypical knowledge. At last, we theoretically and experimentally demonstrate that the proposed prototypical knowledge oriented adaptation approach provides effective guidance on distribution alignment and alleviation on over-adaptation. The proposed approach shows competitive performance with state-of-the-art methods on two cross-domain segmentation tasks.