I am not sure that I understand what you're doing but assuming that the ROC analysis is about predicting survival at 1, resp. 2 years, then it is not doing the same thing as the logrank test. With the ROC analysis you're testing prediction of survival at a particular time point for each group separately, with the logrank test you're comparing the survival distributions between two groups. If the survival distributions differ significantly at 5 years but not at one year then your model shouldn't predict survival at one year very well but the logrank test should still pick up a difference since it considers the distributions (i.e. at all time points). I also think that your assumption of good p-value being linked to good AUC relies on the mistake that p-values are indicative of the strength of an effect. There are plenty of cases where a detectable effect doesn't translate into a strong statistical signal. Although what constitutes a good AUC value depends on the context, I would generally consider AUC below 0.8 to be not very good especially in a medical context where false positives can be costly. Finally, it seems likely that your analysis is underpowered and you may need more patients.