Derandomization of vertex cover algorithm
First, let us repeat the analysis of the algorithm.

Fix some optimal vertex cover OPT, with cost $O$.

Let $S$ be the cost of the vertex cover produced by the algorithm.

Let $A_e$ be the indicator for the event "when edge $e$ was considered, the algorithm added a vertex belonging to OPT", and let $A = \sum_e A_e$.

Let $B_e$ be the indicator for the event "when edge $e$ was considered, the algorithm added a vertex not belonging to OPT", and let $B = \sum_e B_e$.

Thus $S = A+B$ and $A \leq O$.

The idea of the analysis is that $\Pr[B_e] \leq \Pr[A_e]$. To see this, consider what happens when vertex $e$ is considered. If $e$ is already covered by vertices already chosen, then $A_e = B_e = 0$. If both endpoints of $e$ belong to OPT, then $A_e = 1$ and $B_e = 0$. Otherwise, $\Pr[A_e] = \Pr[B_e] = 1/2$. In all cases, $\Pr[B_e] \leq \Pr[A_e]$.

Since $\Pr[B_e] \leq \Pr[A_e]$, we have $\mathbb{E}[B] \leq \mathbb{E}[A]$. Therefore
$$
\mathbb{E}[S] = \mathbb{E}[A+B] \leq 2\mathbb{E}[A] \leq 2O.
$$

How would we apply the method of conditional expectations? Here are two options:


Given the choice of which endpoint is taken, compute the expected value of $S$. If $z \in \{x,y\}$ was chosen, then we remove all edges adjacent to $z$, and then run the algorithm as usual. This reduces the problem to computing $\mathbb{E}[S]$, which is the expected number of edges which are not covered when their time comes. It's not so clear how to compute $\mathbb{E}[S]$.
The same, but instead of computed $\mathbb{E}[S]$ exactly, compute an approximation which is good enough to obtain a 2-approximation. Let $O_z$ be the optimum solution after removing all edges adjacent to $z$. Then
$$
\mathbb{E}[S \mid x] \leq 1 + 2O_x, \quad
\mathbb{E}[S \mid y] \leq 1 + 2O_y.
$$
If $x$ belongs to OPT then $O_x = O-1$, and otherwise $O_x \leq O$. Thus the average of both bounds is at most
$$
\frac{1+2(O-1)+1+2O}{2} = 2O,
$$
since at least one of $x,y$ belongs to OPT. Therefore, if we choose the vertex that minimizes $O_x,O_y$, then the resulting algorithm will produce a 2-approximation. Unfortunately, it's not clear how to compute $O_x,O_y$ (indeed, this ought to be hard).


In summary, it's not so clear how to apply the method of conditional expectations.